A potential direction to handle it is transferring knowledge from a labeled source domain to a different unlabeled target domain, which is known as unsupervised domain adaptation [1]}, leveraging data from both domains.
However, very few works have studied unsupervised domain adaptation on RC tasks. Although [2]} adapted models using a vanilla self-training, its self-labeling approach cannot ensure the labeling accuracy on the target domain that differs much from the source domain. Besides, it is only applied to some small RC datasets, so its effectiveness on large-scale datasets remains unclear and no general representation is learned. Research on large datasets is more meaningful, since they contains more different patterns than small ones. They pose a greater challenge and better fitting realistic conditions, being the basis to build strong deep neural models. In addition, analyzing the possible influential factors for transfer is also necessary, which provides guide for adaptation. Nevertheless, very limited works contribute to it [3]}.
